GFA-NN

Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities

Setup

1. Make sure the Datasets are existent in "./data"

2. Before executing the training, generate pre-preocessed files by

running: ./extract_graph_features/process.sh

3. With the current limitation on GPU memories, we made a multi core

version (5 gpu cores) to allow running the model on the larger dataset (for example biokg). This version of the model is in the folder: ./5_gpu_version_of_model_for_large_datasets

4. Please check the paper Appendix for the best hyper-parameters.

Example run:

FB15k237:

python run.py  --do_train --do_test -save ../experiments/kge_baselines_fb237 --data_path ../data/FB15K237  --model MDE  -n 1000 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 300000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid  -node_feat_path ../data/FB15K237/train_node_features --cuda -psi 15.0

biokg:

python run_with5_gpu.py --init_checkpoint ../experiments/kge_baselines_biokg_400_600_850_2 --do_train --do_test -save ../experiments/kge_baselines_biokg_400_600_850 --data_path ../data/biokg  --model MDE  -n 850 -b 600 -d 400 -g 2.5 -a 2.5 -adv -lr .0005 --max_steps 700000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid  -node_feat_path ../data/biokg/train_node_features --cuda -psi 14.0

Citation :

If you use the model, please cite the following paper:

@inproceedings{gfa2021ECML,
  title={Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities},
  author={Sadeghi, Afshin and Collarana, Diego and  Graux, Damien and Lehmann, Jens},
  booktitle={European Conference on Machine Learning and Data Mining, ECML PKDD 2021},
  year={2021}
}